Assignment 4

Nathan Grimes

3/2/2022

Task 1

Students need to include an overview section that briefly summarizes the dataset.

spill_raw<-read_csv(here("Assignment 4","data","Oil_Spill_Incident_Tracking_[ds394].csv"))

fish_raw<-read_csv(here("Assignment 4","data","willamette_fish_passage.csv"))

ca_counties_sf<-read_sf(here("Assignment 4","data","ca_counties","CA_Counties_TIGER2016.shp"))

Start data wrangling for graphs

ca_subset_sf<-ca_counties_sf %>% 
  janitor::clean_names() %>% 
  select(county_name=name,land_area=aland)

#Match the CRS 
ca_crs<-st_crs(ca_subset_sf)

spill_sf<-st_as_sf(spill_raw,coords=c("X","Y"),crs=ca_crs)
tmap_mode(mode="view")

tm_shape(ca_subset_sf)+
  tm_fill("land_area",palette="BuGn")+
  tm_shape(spill_sf)+
  tm_dots()
spill_count<-spill_raw %>% 
  filter(INLANDMARI=="Inland") %>% 
  group_by(LOCALECOUN) %>% 
  summarize(oil_count=n()) %>% 
  rename(county_name=LOCALECOUN)

oil_sf<-left_join(ca_subset_sf,spill_count,by="county_name")

ggplot()+
  geom_sf(data=oil_sf,aes(fill=oil_count))

library(urbnmapr)
library(stringr)
ca_counties<-counties %>% 
  filter(state_name=="California") %>% 
  mutate_at("county_name",str_replace," County","") %>% 
  mutate_at("county_name",str_squish)


oil_county<-left_join(ca_counties,spill_count,by="county_name")

ggplot(data=oil_county,mapping=aes(long,lat,group=group,fill=oil_count))+
  geom_polygon(color="#ffffff",size=0.25)+
  scale_fill_viridis_c()+
  coord_map(projection = "albers",lat0=39,lat1=45)

Optional Work

oil_sp<-as(spill_sf,"Spatial")

oil_ppp<-as.ppp.SpatialPointsDataFrame(oil_sp)

ca_sp<-as(ca_subset_sf,"Spatial")
ca_win<-as(ca_sp,"owin")

oil_full<-ppp(oil_ppp$x,oil_ppp$y,window=ca_win)

r_vec<-seq(0,11,by=.1)
gfunction<-envelope(oil_full,fun=Gest,r=r_vec,nsim=100,nrank=2)

gfunction_long<- gfunction %>% as.data.frame() %>% pivot_longer(cols=obs:hi,names_to = "model",values_to = "g_val")

ggplot(data=gfunction_long,aes(x=r,y=g_val,group=model))+geom_line(aes(color=model))

Task 2 Fish migration

OG Time Series

fish<-fish_raw %>% 
  janitor::clean_names() %>% 
  mutate(date=lubridate::mdy(date)) %>% 
  as_tsibble(key=NULL,index=date) %>% 
  pivot_longer(!c(project,date),names_to = "species",values_to = "fish_ob") %>% 
  filter(species %in% c("coho","jack_coho","steelhead")) %>% 
  replace_na(list(fish_ob=0))
  

ggplot(data=fish,aes(x=date,y=fish_ob))+
  geom_line(aes(color=species))+
  scale_color_manual(values=c("blue","red","green"))

Season plots

fish_season<-fish %>% 
  index_by(yr_mo=~yearmonth(.)) %>% 
  group_by(species) %>% 
  summarize(monthly_count=sum(fish_ob))


fish_season %>% 
  ggplot(aes(x=year(yr_mo),y=monthly_count))+
  geom_line(aes(color=species))+
  facet_wrap(~month(yr_mo))

fish_season %>% gg_season(y=monthly_count)

Annual counts

fish_count<-fish %>% 
  index_by(date) %>% 
  group_by(species) %>%
  summarize(count=sum(fish_ob))

ggplot(data=fish_count)+
  geom_line(aes(x=date,y=count,color=species))